Biostatistical analysis part 2
AGC
2 2 2022
Last updated: 2023-08-07
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#get sample data
samples.integrated@meta.data %>% as.data.frame() -> samplemeta
spacial_data= samples.microglia@meta.data %>% as.data.frame() %>% select(contains("spatial"))
samplemeta <- cbind(samplemeta, spacial_data[rownames(samplemeta),])
# convert to correct data type
# define genotype as is
samplemeta$Genotype_corr = factor(samplemeta$Genotype=="WT", levels=c(F,T), labels = c("APPPS1+", "WT"))
samplemeta$Genotype_corr = relevel(samplemeta$Genotype_corr, ref="WT")
samplemeta$methoxy = factor(samplemeta$Methoxy=="pos", levels=c(T,F), labels = c("MX04+", "MX04-"))
samplemeta$Treatment = as.factor(samplemeta$Treatment)
samplemeta$Treatment = relevel(samplemeta$Treatment, ref="Ctrl")
samplemeta$Mouse_ID = as.factor(samplemeta$Mouse_ID)
samplemeta$Sex = as.factor(samplemeta$Gender)
samplemeta$Brain_region = as.factor(samplemeta$Brain_region)
samplemeta$Celltype = as.factor(samplemeta$Celltype)
samples.integrated@meta.data = samplemeta
nCells=nrow(samplemeta)
nMice=nlevels(samplemeta$Mouse_ID)
nCelltypes=nlevels(samplemeta$Celltype)Sample descriptive:
Data contains a total of 562 Cells from 9.
Cells per Mouse
Cells per Strain
table(Mouse_ID=samplemeta$Mouse_ID) %>% as.data.frame() %>% display_tab()Cells per Genotype
table(Genotype=samplemeta$Genotype_corr, Treatment=samplemeta$Treatment) %>% as.data.frame() %>% display_tab()table(Genotype=samplemeta$Genotype_corr, Celltype=samplemeta$Celltype) %>%
as.data.frame() %>% display_tab()## add spatial cell types
table(Genotype=samplemeta$Genotype_corr, Sp_Controls=samplemeta$Control_spatial_binary) %>%
as.data.frame() %>% display_tab()table(Genotype=samplemeta$Genotype_corr, Sp_Stroke=samplemeta$Stroke_spatial_binary) %>%
as.data.frame() %>% display_tab()table(Genotype=samplemeta$Genotype_corr,
Celltype=samplemeta$Celltype, Treatment=samplemeta$Treatment
) %>%
as.data.frame() %>% display_tab()samplemeta$condition=paste0(samplemeta$Genotype_corr,"_", samplemeta$Treatment)
samples.integrated@meta.data = samplemeta
sce=as.SingleCellExperiment(samples.integrated)
sce <- slingshot(sce, clusterLabels = 'Celltype', reducedDim = 'UMAP', start.clus="Microglia_5")
samplemeta$Pseudotime_1 <- sce$slingPseudotime_1
samplemeta$Pseudotime_2 <- sce$slingPseudotime_2
samplemeta$Pseudotime_3 <- sce$slingPseudotime_3
samples.integrated@meta.data = samplemeta
variables=c("Celltype","Sex", "Age","Genotype_corr", "Treatment", "condition","Phase","Control_spatial_binary","Stroke_spatial_binary", "Pseudotime_1","Pseudotime_2","Pseudotime_3","Brain_region","nCount_RNA","pseudoaligned_reads", "percent.mt", "percent.rp", "Mouse_ID")Descriptive stats across Cell type
res = compareGroups(Celltype~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)| Microglia_1 | Microglia_2 | Microglia_3 | Microglia_4 | Granulocytes | Microglia_5 | Mono/Mac | p.overall | |
|---|---|---|---|---|---|---|---|---|
| N=372 | N=86 | N=57 | N=8 | N=4 | N=15 | N=20 | ||
| Sex: | 0.276 | |||||||
| f | 73 (19.6%) | 20 (23.3%) | 17 (29.8%) | 2 (25.0%) | 2 (50.0%) | 5 (33.3%) | 5 (25.0%) | |
| m | 299 (80.4%) | 66 (76.7%) | 40 (70.2%) | 6 (75.0%) | 2 (50.0%) | 10 (66.7%) | 15 (75.0%) | |
| Age: | . | |||||||
| 37 weeks | 226 (60.8%) | 56 (65.1%) | 41 (71.9%) | 6 (75.0%) | 2 (50.0%) | 7 (46.7%) | 11 (55.0%) | |
| 38 weeks | 44 (11.8%) | 8 (9.30%) | 13 (22.8%) | 2 (25.0%) | 2 (50.0%) | 4 (26.7%) | 3 (15.0%) | |
| 40 weeks | 102 (27.4%) | 22 (25.6%) | 3 (5.26%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 6 (30.0%) | |
| Genotype_corr: | . | |||||||
| WT | 170 (45.7%) | 43 (50.0%) | 42 (73.7%) | 8 (100%) | 4 (100%) | 8 (53.3%) | 15 (75.0%) | |
| APPPS1+ | 202 (54.3%) | 43 (50.0%) | 15 (26.3%) | 0 (0.00%) | 0 (0.00%) | 7 (46.7%) | 5 (25.0%) | |
| Treatment: | . | |||||||
| Ctrl | 272 (73.1%) | 56 (65.1%) | 52 (91.2%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (40.0%) | |
| Stroke | 100 (26.9%) | 30 (34.9%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 12 (60.0%) | |
| condition: | . | |||||||
| APPPS1+_Ctrl | 164 (44.1%) | 28 (32.6%) | 10 (17.5%) | 0 (0.00%) | 0 (0.00%) | 6 (40.0%) | 1 (5.00%) | |
| APPPS1+_Stroke | 38 (10.2%) | 15 (17.4%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 1 (6.67%) | 4 (20.0%) | |
| WT_Ctrl | 108 (29.0%) | 28 (32.6%) | 42 (73.7%) | 8 (100%) | 4 (100%) | 5 (33.3%) | 7 (35.0%) | |
| WT_Stroke | 62 (16.7%) | 15 (17.4%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3 (20.0%) | 8 (40.0%) | |
| Phase: | . | |||||||
| G1 | 159 (42.7%) | 36 (41.9%) | 19 (33.3%) | 0 (0.00%) | 2 (50.0%) | 10 (66.7%) | 9 (45.0%) | |
| G2M | 95 (25.5%) | 18 (20.9%) | 17 (29.8%) | 2 (25.0%) | 2 (50.0%) | 2 (13.3%) | 6 (30.0%) | |
| S | 118 (31.7%) | 32 (37.2%) | 21 (36.8%) | 6 (75.0%) | 0 (0.00%) | 3 (20.0%) | 5 (25.0%) | |
| Control_spatial_binary: | 0.080 | |||||||
| FALSE | 338 (91.1%) | 75 (87.2%) | 57 (100%) | 8 (100%) | 4 (100%) | 15 (100%) | 8 (100%) | |
| TRUE | 33 (8.89%) | 11 (12.8%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | |
| Stroke_spatial_binary: | . | |||||||
| FALSE | 240 (64.7%) | 55 (64.0%) | 53 (93.0%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (100%) | |
| TRUE | 131 (35.3%) | 31 (36.0%) | 4 (7.02%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 0 (0.00%) | |
| Pseudotime_1 | 3.76 (2.36) | 8.25 (1.08) | 11.2 (0.97) | . (.) | . (.) | 0.40 (0.29) | 14.8 (0.99) | <0.001 |
| Pseudotime_2 | 3.50 (1.53) | 8.35 (1.12) | 9.64 (0.22) | 13.1 (0.48) | . (.) | 0.52 (0.29) | . (.) | <0.001 |
| Pseudotime_3 | 4.09 (1.87) | . (.) | 8.51 (0.23) | . (.) | 22.1 (0.04) | 0.40 (0.30) | 9.99 (0.17) | <0.001 |
| Brain_region: | . | |||||||
| Cortex | 272 (73.1%) | 56 (65.1%) | 52 (91.2%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (40.0%) | |
| Lesion | 100 (26.9%) | 30 (34.9%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 12 (60.0%) | |
| nCount_RNA | 158416 (103720) | 194411 (106729) | 164921 (154435) | 217767 (205467) | 204215 (86217) | 165673 (126013) | 162375 (90426) | 0.154 |
| pseudoaligned_reads | 158947 (104087) | 194836 (106825) | 165764 (154492) | 218138 (205598) | 204425 (86225) | 166053 (126105) | 163005 (90604) | 0.160 |
| percent.mt | 1.90 (1.35) | 2.14 (1.78) | 1.44 (1.07) | 1.66 (1.04) | 0.98 (1.13) | 2.01 (1.01) | 2.04 (1.27) | 0.089 |
| percent.rp | 2.21 (1.29) | 3.05 (2.17) | 1.48 (1.34) | 0.98 (0.41) | 1.02 (0.50) | 2.03 (1.25) | 3.16 (2.30) | <0.001 |
| Mouse_ID: | . | |||||||
| 256#1022 | 33 (8.87%) | 9 (10.5%) | 13 (22.8%) | 2 (25.0%) | 0 (0.00%) | 1 (6.67%) | 2 (10.0%) | |
| 256#1023 | 31 (8.33%) | 11 (12.8%) | 16 (28.1%) | 4 (50.0%) | 2 (50.0%) | 0 (0.00%) | 2 (10.0%) | |
| 364#469 | 44 (11.8%) | 8 (9.30%) | 13 (22.8%) | 2 (25.0%) | 2 (50.0%) | 4 (26.7%) | 3 (15.0%) | |
| 386 | 16 (4.30%) | 4 (4.65%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 4 (20.0%) | |
| 387 | 9 (2.42%) | 3 (3.49%) | 1 (1.75%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 2 (10.0%) | |
| 388 | 77 (20.7%) | 15 (17.4%) | 2 (3.51%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 0 (0.00%) | |
| 409 | 46 (12.4%) | 11 (12.8%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3 (20.0%) | 4 (20.0%) | |
| 457 | 87 (23.4%) | 13 (15.1%) | 8 (14.0%) | 0 (0.00%) | 0 (0.00%) | 2 (13.3%) | 1 (5.00%) | |
| 461 | 29 (7.80%) | 12 (14.0%) | 4 (7.02%) | 0 (0.00%) | 0 (0.00%) | 1 (6.67%) | 2 (10.0%) |
export2xls(export_table,paste0(home,"/docs/Descriptives.xlsx"))
## compare Binary Spatial Controls
res = compareGroups(Control_spatial_binary~., data = samplemeta[,variables], max.ylev = 10)Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)| FALSE | TRUE | p.overall | |
|---|---|---|---|
| N=505 | N=44 | ||
| Celltype: | 0.080 | ||
| Microglia_1 | 338 (66.9%) | 33 (75.0%) | |
| Microglia_2 | 75 (14.9%) | 11 (25.0%) | |
| Microglia_3 | 57 (11.3%) | 0 (0.00%) | |
| Microglia_4 | 8 (1.58%) | 0 (0.00%) | |
| Granulocytes | 4 (0.79%) | 0 (0.00%) | |
| Microglia_5 | 15 (2.97%) | 0 (0.00%) | |
| Mono/Mac | 8 (1.58%) | 0 (0.00%) | |
| Sex: | 0.046 | ||
| f | 118 (23.4%) | 4 (9.09%) | |
| m | 387 (76.6%) | 40 (90.9%) | |
| Age: | 0.019 | ||
| 37 weeks | 316 (62.6%) | 27 (61.4%) | |
| 38 weeks | 75 (14.9%) | 1 (2.27%) | |
| 40 weeks | 114 (22.6%) | 16 (36.4%) | |
| Genotype_corr: | <0.001 | ||
| WT | 274 (54.3%) | 9 (20.5%) | |
| APPPS1+ | 231 (45.7%) | 35 (79.5%) | |
| Treatment: | 0.795 | ||
| Ctrl | 375 (74.3%) | 34 (77.3%) | |
| Stroke | 130 (25.7%) | 10 (22.7%) | |
| condition: | <0.001 | ||
| APPPS1+_Ctrl | 178 (35.2%) | 29 (65.9%) | |
| APPPS1+_Stroke | 53 (10.5%) | 6 (13.6%) | |
| WT_Ctrl | 197 (39.0%) | 5 (11.4%) | |
| WT_Stroke | 77 (15.2%) | 4 (9.09%) | |
| Phase: | 0.215 | ||
| G1 | 217 (43.0%) | 13 (29.5%) | |
| G2M | 123 (24.4%) | 14 (31.8%) | |
| S | 165 (32.7%) | 17 (38.6%) | |
| Stroke_spatial_binary: | <0.001 | ||
| FALSE | 371 (73.5%) | 8 (18.2%) | |
| TRUE | 134 (26.5%) | 36 (81.8%) | |
| Pseudotime_1 | 5.67 (3.78) | 4.63 (3.33) | 0.086 |
| Pseudotime_2 | 4.63 (2.81) | 4.58 (3.13) | 0.916 |
| Pseudotime_3 | 4.35 (2.84) | 3.67 (2.26) | 0.120 |
| Brain_region: | 0.795 | ||
| Cortex | 375 (74.3%) | 34 (77.3%) | |
| Lesion | 130 (25.7%) | 10 (22.7%) | |
| nCount_RNA | 161418 (112945) | 214465 (106144) | 0.003 |
| pseudoaligned_reads | 161849 (113014) | 214704 (106166) | 0.003 |
| percent.mt | 1.76 (1.27) | 3.14 (2.04) | <0.001 |
| percent.rp | 2.05 (1.34) | 4.20 (2.00) | <0.001 |
| Mouse_ID: | . | ||
| 256#1022 | 58 (11.5%) | 2 (4.55%) | |
| 256#1023 | 64 (12.7%) | 2 (4.55%) | |
| 364#469 | 75 (14.9%) | 1 (2.27%) | |
| 386 | 19 (3.76%) | 1 (2.27%) | |
| 387 | 10 (1.98%) | 3 (6.82%) | |
| 388 | 85 (16.8%) | 12 (27.3%) | |
| 409 | 58 (11.5%) | 3 (6.82%) | |
| 457 | 93 (18.4%) | 17 (38.6%) | |
| 461 | 43 (8.51%) | 3 (6.82%) |
export2xls(export_table,paste0(home,"/docs/Descriptives_sp_Control.xlsx"))
## compare Binary Spatial Stroke
res = compareGroups(Stroke_spatial_binary~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)| FALSE | TRUE | p.overall | |
|---|---|---|---|
| N=379 | N=170 | ||
| Celltype: | . | ||
| Microglia_1 | 240 (63.3%) | 131 (77.1%) | |
| Microglia_2 | 55 (14.5%) | 31 (18.2%) | |
| Microglia_3 | 53 (14.0%) | 4 (2.35%) | |
| Microglia_4 | 8 (2.11%) | 0 (0.00%) | |
| Granulocytes | 4 (1.06%) | 0 (0.00%) | |
| Microglia_5 | 11 (2.90%) | 4 (2.35%) | |
| Mono/Mac | 8 (2.11%) | 0 (0.00%) | |
| Sex: | <0.001 | ||
| f | 103 (27.2%) | 19 (11.2%) | |
| m | 276 (72.8%) | 151 (88.8%) | |
| Age: | <0.001 | ||
| 37 weeks | 242 (63.9%) | 101 (59.4%) | |
| 38 weeks | 76 (20.1%) | 0 (0.00%) | |
| 40 weeks | 61 (16.1%) | 69 (40.6%) | |
| Genotype_corr: | <0.001 | ||
| WT | 249 (65.7%) | 34 (20.0%) | |
| APPPS1+ | 130 (34.3%) | 136 (80.0%) | |
| Treatment: | 0.003 | ||
| Ctrl | 297 (78.4%) | 112 (65.9%) | |
| Stroke | 82 (21.6%) | 58 (34.1%) | |
| condition: | <0.001 | ||
| APPPS1+_Ctrl | 97 (25.6%) | 110 (64.7%) | |
| APPPS1+_Stroke | 33 (8.71%) | 26 (15.3%) | |
| WT_Ctrl | 200 (52.8%) | 2 (1.18%) | |
| WT_Stroke | 49 (12.9%) | 32 (18.8%) | |
| Phase: | 0.573 | ||
| G1 | 155 (40.9%) | 75 (44.1%) | |
| G2M | 93 (24.5%) | 44 (25.9%) | |
| S | 131 (34.6%) | 51 (30.0%) | |
| Control_spatial_binary: | <0.001 | ||
| FALSE | 371 (97.9%) | 134 (78.8%) | |
| TRUE | 8 (2.11%) | 36 (21.2%) | |
| Pseudotime_1 | 5.98 (3.85) | 4.51 (3.25) | <0.001 |
| Pseudotime_2 | 4.67 (2.74) | 4.54 (3.03) | 0.669 |
| Pseudotime_3 | 4.40 (3.03) | 4.08 (2.25) | 0.254 |
| Brain_region: | 0.003 | ||
| Cortex | 297 (78.4%) | 112 (65.9%) | |
| Lesion | 82 (21.6%) | 58 (34.1%) | |
| nCount_RNA | 161384 (122017) | 175222 (90282) | 0.139 |
| pseudoaligned_reads | 161834 (122084) | 175563 (90328) | 0.143 |
| percent.mt | 1.54 (1.03) | 2.62 (1.78) | <0.001 |
| percent.rp | 1.59 (1.06) | 3.62 (1.46) | <0.001 |
| Mouse_ID: | . | ||
| 256#1022 | 58 (15.3%) | 2 (1.18%) | |
| 256#1023 | 66 (17.4%) | 0 (0.00%) | |
| 364#469 | 76 (20.1%) | 0 (0.00%) | |
| 386 | 8 (2.11%) | 12 (7.06%) | |
| 387 | 6 (1.58%) | 7 (4.12%) | |
| 388 | 47 (12.4%) | 50 (29.4%) | |
| 409 | 41 (10.8%) | 20 (11.8%) | |
| 457 | 50 (13.2%) | 60 (35.3%) | |
| 461 | 27 (7.12%) | 19 (11.2%) |
export2xls(export_table,paste0(home,"/docs/Descriptives_sp_Stroke.xlsx"))download data as excel file here
Preprocessing
include only genes that are different across samples (5 974 excluded) include only genes with more than 10 reads in at least 10 cells in at least one Celltype (21 733 transcripts excluded) exclude all cells with less than 10000 reads (none excluded)
649 cells and 11 623 transcripts analyzed
#get normalized counts
# question to Desiree hat the Seurat object been initialized with normalized data?
counts <- samples.integrated@assays$RNA@counts %>% as.data.frame()
# drop no variance data and sort by samplemeta
counts <- counts[apply(counts,1, sd) > 0, rownames(samplemeta)]
# drop genes with low detection rate (more than 5 counts per cell)
counts_per_celltype=apply(counts, 1, function(x){tapply(x, samplemeta$Celltype, function(z){sum(z>10,na.rm=T)})}) %>% as.data.frame()
# keep RNAs with at least 10 cells with good expression
idxr=which(colSums(counts_per_celltype)>10)
idxc=which(colSums(counts)>10000)
counts = counts[idxr,idxc]
samplemeta=samplemeta[colnames(counts), ]Overview tables
samplemeta$corrGenotype_Treatment<-
paste0(samplemeta$Genotype_corr," ", samplemeta$Treatment)
sumstat = counts %>% t() %>%
aggregate(by=samplemeta["corrGenotype_Treatment"], mean) %>%
t() %>% as.data.frame()
names(sumstat) <- paste0("mean",sumstat[1,])
sumstat<-sumstat[-1,]
sumstatCelltype = counts %>% t() %>%
aggregate(by=samplemeta["Celltype"], mean) %>%
t() %>% as.data.frame()
names(sumstatCelltype) <- paste0("mean",sumstatCelltype[1,])
sumstatCelltype<-sumstatCelltype[-1,]
sumstat_spStroke = counts %>% t() %>%
aggregate(by=samplemeta["Stroke_spatial_binary"], mean) %>%
t() %>% as.data.frame()
names(sumstat_spStroke) <- paste0("mean_spStrk",sumstat_spStroke[1,])
sumstat_spStroke<-sumstat_spStroke[-1,]
sumstat_spCtrl = counts %>% t() %>%
aggregate(by=samplemeta["Control_spatial_binary"], mean) %>%
t() %>% as.data.frame()
names(sumstat_spCtrl) <- paste0("mean_spStrk",sumstat_spCtrl[1,])
sumstat_spCtrl<-sumstat_spCtrl[-1,]
Geno.Treatment_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$corrGenotype_Treatment) %>% unlist()
return(as.numeric(res["p.value"]))
})
Geno.Treatment_kruskal_fdr = p.adjust(Geno.Treatment_kruskal, method = "fdr")
Celltype_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$Celltype) %>% unlist()
return(as.numeric(res["p.value"]))
})
Celltype_kruskal_fdr = p.adjust(Celltype_kruskal, method = "fdr")
Control_spatial_binary_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$Control_spatial_binary) %>% unlist()
return(as.numeric(res["p.value"]))
})
Control_spatial_binary_kruskal_fdr = p.adjust(Control_spatial_binary_kruskal, method = "fdr")
Stroke_spatial_binary_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$Stroke_spatial_binary) %>% unlist()
return(as.numeric(res["p.value"]))
})
Stroke_spatial_binary_kruskal_fdr = p.adjust(Stroke_spatial_binary_kruskal, method = "fdr")
summarytab=cbind(sumstat, Geno.Treatment_kruskal, Geno.Treatment_kruskal_fdr ,
sumstatCelltype, Celltype_kruskal, Celltype_kruskal_fdr,
sumstat_spCtrl, Control_spatial_binary_kruskal, Control_spatial_binary_kruskal_fdr,
sumstat_spStroke,Stroke_spatial_binary_kruskal,Stroke_spatial_binary_kruskal_fdr)
summarytab %>% display_tab()Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlHierarchical clustering
to check where the variance in the data comes from
log2_cpm = log2(counts+1)
varsset=apply(log2_cpm, 1, var)
cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:1000],])
distance = dist(cpm.sel.trans)
sampleDistMatrix <- as.matrix(distance)
#colors for plotting heatmap
colors <- rev(colorRampPalette(brewer.pal(9, "Spectral"))(255))
colors=jetcolors(255)
colors=viridis(255)
cellcol = Dark8[1:nlevels(samplemeta$Celltype)]
names(cellcol) = levels(samplemeta$Celltype)
Stroke_sp = Dark8[1:nlevels(samplemeta$Stroke_spatial_binary)]
names(Stroke_sp) = levels(samplemeta$Stroke_spatial_binary)
Ctrl_sp = Dark8[1:nlevels(samplemeta$Control_spatial_binary)]
names(Ctrl_sp) = levels(samplemeta$Control_spatial_binary)
genotypecol = brewer.pal(4,"Accent")[c(1:nlevels(samplemeta$Genotype_corr))]
names(genotypecol) = levels(samplemeta$Genotype_corr)
strokecol = brewer.pal(5,"Set2")[1:nlevels(samplemeta$Treatment)+2]
names(strokecol) = levels(samplemeta$Treatment)
mousecol = brewer.pal(9,"Set1")[1:nlevels(samplemeta$Mouse_ID)]
names(mousecol) = levels(samplemeta$Mouse_ID)
braincol = brewer.pal(3,"Set2")[1:nlevels(samplemeta$Brain_region)]
names(braincol) = levels(samplemeta$Brain_region)
samplemeta$Age = as.factor(samplemeta$Age)
Agecol = colorRampPalette(c("dodgerblue",
"dodgerblue4"))(nlevels(samplemeta$Age))[1:nlevels(samplemeta$Age)]
names(Agecol) = levels(samplemeta$Age)
ann_colors = list(
Genotype_corr = genotypecol,
Mouse_ID = mousecol,
Brain_region = braincol,
Celltype=cellcol,
Treatment=strokecol,
Age=Agecol,
Control_spatial_binary = Ctrl_sp,
Stroke_spatial_binary =Stroke_sp)
labels = samplemeta[,c("Genotype_corr","Mouse_ID", "Age", "Brain_region", "Celltype",
"Control_spatial_binary","Stroke_spatial_binary","Treatment")] %>%
mutate_all(as.character) %>% as.data.frame()
rownames(labels)=rownames(samplemeta)
pheatmap(sampleDistMatrix,
clustering_distance_rows = distance,
clustering_distance_cols = distance,
clustering_method = "ward.D2",
scale ="none",
legend = F,
show_rownames=F, show_colnames = F,
border_color = NA,
annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
main = "D62 top1000 Distances normalized log2 counts")
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 51b35c8 | achiocch | 2022-04-26 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| f21fdf7 | achiocch | 2022-03-30 |
| 858d646 | achiocch | 2022-02-03 |
Genotype X Treatment Statistical modelling
the logFC should not be interpreted on its own without the specific post hoc tests (see app below to check the individual genes) In any case the value here would correspond to the APPPS1+ with Stroke against all others.
Heatmaps show standardized deviations from mean across all cells, trimmed to 2 standard deviations ( i.e. values above 2 SD are set to 2 SD). this allows to see more subtle changes better.
getres=function(Celltype="Specify",
Hypothesis="~1+Sex+Genotype_corr*Treatment",
Target="Genotype_corrAPPPS1+:TreatmentStroke",
Randomeffect=NULL){
res= comparison_rand(designform =Hypothesis,
randomeffect = Randomeffect,
Samples = samplemeta$Celltype==Celltype,
log_cpm = log2_cpm,
samplesdata = samplemeta,
target=Target)
labels = samplemeta[samplemeta$Celltype==Celltype,c("Treatment","Age", "Genotype_corr","Mouse_ID", "Brain_region", "Celltype","Control_spatial_binary", "Stroke_spatial_binary")] %>%
mutate_all(as.character) %>% as.data.frame()
labels=labels %>% arrange(Treatment,Genotype_corr)
plotdata=log2_cpm[res$adj.P.Val<0.05,rownames(labels)]
plotdata = apply(plotdata, 2, trimmed_scaled)
pheatmap(plotdata,
#clustering_method = "ward.D2",
cluster_cols = F,
cluster_rows = T,
scale ="row",
show_rownames=F, show_colnames = F,
legend=T,
border_color = NA,
#annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
breaks = seq(-2,2, length.out=254),
main = "D62 Distances normalized log2 counts")
return(res)
}
getresspatio=function(Celltype="Specify",
Hypothesis="~1+Sex+Genotype_corr*Treatment",
Target="Genotype_corrAPPPS1+:TreatmentStroke",
Randomeffect=NULL){
idx=NA
if(Celltype =="Stroke_spatial_binary"){
idx=samplemeta$Stroke_spatial_binary==T
}
if(Celltype =="Control_spatial_binary"){
idx=samplemeta$Control_spatial_binary==T
}
idx[is.na(idx)]=F
res=comparison_rand(designform =Hypothesis,
randomeffect = Randomeffect,
Samples = idx,
log_cpm = log2_cpm,
samplesdata = samplemeta,
target=Target)
labels = samplemeta[idx,c("Treatment","Age", "Genotype_corr","Mouse_ID", "Brain_region", "Celltype", "Control_spatial_binary", "Stroke_spatial_binary")] %>%
mutate_all(as.character) %>% as.data.frame()
labels=labels %>% arrange(Treatment,Genotype_corr)
plotdata=log2_cpm[res$adj.P.Val<0.05,rownames(labels)]
plotdata = apply(plotdata, 2, trimmed_scaled)
pheatmap(plotdata,
#clustering_method = "ward.D2",
cluster_cols = F,
cluster_rows = T,
scale ="row",
show_rownames=F, show_colnames = F,
legend=T,
border_color = NA,
#annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
breaks = seq(-2,2, length.out=254),
main = "D62 Distances normalized log2 counts")
return(res)
}
generate_output=function(CT="Celltype",mode="Celltype", ...){
respath=paste0(home, "/docs/LMER_",CT,".xlsx")
if(mode == "Spatial"){
analysis = getresspatio(Celltype = CT)
}
if (mode == "Celltype"){
analysis = getres(Celltype = CT)}
analysis_sig = analysis[analysis$adj.P.Val<0.05,]
analysis_sig %>% display_tab()
write.xlsx2(analysis_sig, file =respath , sheetName = "significant genes")
ResGO = getGOresults(rownames(analysis_sig),
rownames(analysis),
"mmusculus")
if(length(ResGO)>0){
p=gostplot(ResGO)
} else{
ResGO=data.frame(result="no significant enrichment identified")
p="no significant enrichment identified"
}
write.xlsx2(ResGO$result, file = respath, sheetName = "GO_enrichment", append=T)
return(list(results=analysis,results_sig=analysis_sig, plot=p))
}Microglia_1
res_output=generate_output(CT = "Microglia_1")
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| 87439a3 | achiocch | 2022-02-21 |
| 79dd0b1 | achiocch | 2022-02-21 |
| 858d646 | achiocch | 2022-02-03 |
[1] "no significant GO terms identified"res_output[["results_sig"]] %>% display_tab()res_output[["plot"]][1] "no significant enrichment identified"Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
Microglia_2
res_output=generate_output("Microglia_2")
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| 87439a3 | achiocch | 2022-02-21 |
| 79dd0b1 | achiocch | 2022-02-21 |
| 858d646 | achiocch | 2022-02-03 |
[1] "no significant GO terms identified"res_output[["results_sig"]] %>% display_tab()res_output[["plot"]][1] "no significant enrichment identified"Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
Microglia_3
idx=samplemeta$Celltype=="Microglia_3"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 42 10
Stroke 0 5table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 0.0007172
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
3.138602 Inf
sample estimates:
odds ratio
Inf Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
Microglia_4
calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference
idx=samplemeta$Celltype=="Microglia_4"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 7 0
Stroke 0 0table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0 Inf
sample estimates:
odds ratio
0 #res_output=generate_output("Microglia_4")
#res_output[["results_sig"]] %>% display_tab()
#res_output[["plot"]]get results table here
Microglia_5
res_output=generate_output("Microglia_5")
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| 87439a3 | achiocch | 2022-02-21 |
| 79dd0b1 | achiocch | 2022-02-21 |
| 858d646 | achiocch | 2022-02-03 |
[1] "no significant GO terms identified"res_output[["results_sig"]] %>% display_tab()res_output[["plot"]][1] "no significant enrichment identified"Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA) get results table here
Granulozytes
calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference
idx=samplemeta$Celltype=="Granulocytes"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 4 0
Stroke 0 0table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0 Inf
sample estimates:
odds ratio
0 # res_output=generate_output("Granulocytes ")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]get results table here
Mono/Mac
idx=samplemeta$Celltype=="Mono/Mac"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 7 1
Stroke 8 4table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 0.6027
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2433699 196.3003974
sample estimates:
odds ratio
3.302108 # res_output=generate_output("T/NK")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]get results table here
Control Spacial
res_output=generate_output(CT = "Control_spatial_binary", mode="Spatial")
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
[1] "no significant GO terms identified"res_output[["results_sig"]] %>% display_tab()res_output[["plot"]][1] "no significant enrichment identified"Strole Spatial
res_output=generate_output("Stroke_spatial_binary", mode="Spatial")
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
[1] "no significant GO terms identified"res_output[["results_sig"]] %>% display_tab()res_output[["plot"]][1] "no significant enrichment identified"Pseudotime analysis
dataset = data.frame(reducedDim(sce, "UMAP"),
cluster = as.character(sce$Celltype),
Stroke_spacial = as.character(sce$Stroke_spatial_binary))
curves <- slingCurves(sce, as.df=T)
p = ggplot(dataset, aes(x=UMAP_1, y=UMAP_2))+
geom_point(aes(col = cluster), size=2)+
geom_point(data=subset(dataset, dataset$Stroke_spacial==T), aes(fill=Stroke_spacial), pch=21)+
scale_fill_manual(values="black")
p = p + geom_path(data = curves %>% arrange(Order), aes(group = Lineage), lwd=1.5)+theme_classic()
p
ggsave(filename = "./docs/Pseudotime_linages.svg", p)Saving 6 x 6 in imageggsave(filename = "./docs/Pseudotime_linages.pdf", p)Saving 6 x 6 in imagedataset$pseudotime_1 <- sce$slingPseudotime_1
dataset$pseudotime_2 <- sce$slingPseudotime_2
dataset$pseudotime_3 <- sce$slingPseudotime_3
dataset$Genotype_Treatment <- sce$Genotype_Treatment
p1 <-ggplot(dataset, aes(x=pseudotime_1))+
geom_density(aes(group=Genotype_Treatment, col=Genotype_Treatment), lwd=2)+
theme_classic()
p = ggplot(dataset, aes(x=UMAP_1, y=UMAP_2))+
geom_point(aes(col = pseudotime_1), size=4)+
geom_point(data=subset(dataset, dataset$Stroke_spacial==T), aes(fill=Stroke_spacial), pch=21)+
scale_fill_manual(values="black")+scale_color_viridis()+theme_classic()
p = p + geom_path(data = curves[curves$Lineage==1,] %>% arrange(Order), aes(group = Lineage), lwd=2)+theme_classic()
plt = ggarrange(p1,p)Warning: Removed 99 rows containing non-finite values (`stat_density()`).plt
ggsave(filename = "./docs/Pseudotime_density_Lin1.svg", p)Saving 12 x 6 in imageggsave(filename = "./docs/Pseudotime_denisty_Lin1.pdf", p)Saving 12 x 6 in imagep1 <-ggplot(dataset, aes(x=pseudotime_2))+
geom_density(aes(group=Genotype_Treatment, col=Genotype_Treatment), lwd=2)+
theme_classic()
p = ggplot(dataset, aes(x=UMAP_1, y=UMAP_2))+
geom_point(aes(col = pseudotime_2), size=4)+
geom_point(data=subset(dataset, dataset$Stroke_spacial==T), aes(fill=Stroke_spacial), pch=21)+
scale_fill_manual(values="black")+scale_color_viridis()+theme_classic()
# manually selected linage 3 here as this fits best to peudotimes values
p = p + geom_path(data = subset(curves, Lineage==2) %>% arrange(Order), aes(group = Lineage), lwd=2)+theme_classic()
plt = ggarrange(p1,p)Warning: Removed 169 rows containing non-finite values (`stat_density()`).plt
ggsave(filename = "./docs/Pseudotime_density_Lin2.svg", p)Saving 8 x 4 in imageggsave(filename = "./docs/Pseudotime_denisty_Lin2.pdf", p)Saving 8 x 4 in imagep1 <-ggplot(dataset, aes(x=pseudotime_3))+
geom_density(aes(group=Genotype_Treatment, col=Genotype_Treatment), lwd=2)+
theme_classic()
p = ggplot(dataset, aes(x=UMAP_1, y=UMAP_2))+
geom_point(aes(col = pseudotime_3), size=4)+
geom_point(data=subset(dataset, dataset$Stroke_spacial==T), aes(fill=Stroke_spacial), pch=21)+
scale_fill_manual(values="black")+scale_color_viridis()+theme_classic()
p = p + geom_path(data = curves[curves$Lineage==3,] %>% arrange(Order), aes(group = Lineage), lwd=2)+theme_classic()
plt = ggarrange(p1,p)Warning: Removed 163 rows containing non-finite values (`stat_density()`).plt
ggsave(filename = "./docs/Pseudotime_density_Lin3.svg", p)Saving 8 x 4 in imageggsave(filename = "./docs/Pseudotime_denisty_Lin3.pdf", p)Saving 8 x 4 in imagePseudotime Lineage 1 association
GOplot <- function(gostresult)
if(is.null(gostresult)){
ggplot() + # Draw ggplot2 plot with text only
annotate("text",
x = 1,
y = 1,
size = 8,
label = "no significant GO-terms associated") +
theme_void()
} else {
gostplot(gostresult)}
sce_Pseudotime1 = samples.integrated[,!is.na(samples.integrated$Pseudotime_1)]
res = comparison_rand(designform ="~1+Sex+Pseudotime_1",
Samples = c(),
log_cpm = log1p(as.data.frame(sce_Pseudotime1@assays$RNA@counts)),
samplesdata = as.data.frame(sce_Pseudotime1@meta.data),
randomeffect = c(),
target = "Pseudotime_1")[1] "Sample length is 0 all samples included"Warning: Zero sample variances detected, have been offset away from zeroanalysis_sig <- res[res$P.Value<0.05,]
analysis_sig %>% display_tab()write.xlsx2(res, file ="./docs/DEX_Pseudotime_Lineage1.xlsx" , sheetName = "DEX genes Lin1")
ResGO = getGOresults(rownames(analysis_sig),
rownames(res),
"mmusculus")
GOplot(ResGO)ResGO$result %>% display_tab()Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlPseudotime Lineage 2 association
sce_Pseudotime3 = samples.integrated[,!is.na(samples.integrated$Pseudotime_2)]
res = comparison_rand(designform ="~1+Sex+Pseudotime_2",
Samples = c(),
log_cpm = log1p(as.data.frame(sce_Pseudotime3@assays$RNA@counts)),
samplesdata = as.data.frame(sce_Pseudotime3@meta.data),
randomeffect = c(),
target = "Pseudotime_2")[1] "Sample length is 0 all samples included"Warning: Zero sample variances detected, have been offset away from zeroanalysis_sig <- res[res$P.Value<0.05,]
analysis_sig %>% display_tab()write.xlsx2(res, file ="./docs/DEX_Pseudotime_Lineage2.xlsx" , sheetName = "DEX genes Lin1")
ResGO = getGOresults(rownames(analysis_sig),
rownames(res),
"mmusculus")
GOplot(ResGO)ResGO$result %>% display_tab()Pseudotime Lineage 3 association
sce_Pseudotime1 = samples.integrated[,!is.na(samples.integrated$Pseudotime_3)]
res = comparison_rand(designform ="~1+Sex+Pseudotime_3",
Samples = c(),
log_cpm = log1p(as.data.frame(sce_Pseudotime1@assays$RNA@counts)),
samplesdata = as.data.frame(sce_Pseudotime1@meta.data),
randomeffect = c(),
target = "Pseudotime_3")[1] "Sample length is 0 all samples included"Warning: Zero sample variances detected, have been offset away from zeroanalysis_sig <- res[res$P.Value<0.05,]
analysis_sig %>% display_tab()write.xlsx2(res, file ="./docs/DEX_Pseudotime_Lineage3.xlsx" , sheetName = "DEX genes Lin1")
ResGO = getGOresults(rownames(analysis_sig),
rownames(res),
"mmusculus")
GOplot(ResGO)ResGO$result %>% display_tab()Gene expression plot
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8 LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8 LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] gprofiler2_0.2.1 lm.beta_1.7-1 pheatmap_1.0.12 RColorBrewer_1.1-3 kableExtra_1.3.4 ggpubr_0.6.0 slingshot_2.6.0 TrajectoryUtils_1.6.0 SingleCellExperiment_1.20.0 princurve_2.1.6 DT_0.27 viridis_0.6.2 viridisLite_0.4.1 xlsx_0.6.5 brms_2.18.0 Rcpp_1.0.10 compareGroups_4.6.0 data.table_1.14.6 SingleR_2.0.0 SeuratObject_4.1.3 Seurat_4.3.0 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.1 tidyverse_1.3.2 DESeq2_1.38.3 SummarizedExperiment_1.28.0 Biobase_2.58.0 MatrixGenerics_1.10.0 matrixStats_0.63.0 GenomicRanges_1.50.2 GenomeInfoDb_1.34.9 IRanges_2.32.0 S4Vectors_0.36.1 BiocGenerics_0.44.0 limma_3.54.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-3 svglite_2.1.1 ps_1.7.2 lmtest_0.9-40 rprojroot_2.0.3 crayon_1.5.2 MASS_7.3-58.2 nlme_3.1-162 backports_1.4.1 posterior_1.3.1 reprex_2.0.2 colourpicker_1.2.0 rlang_1.0.6 XVector_0.38.0 ROCR_1.0-11 readxl_1.4.2 irlba_2.3.5.1 callr_3.7.3 flextable_0.8.5 BiocParallel_1.32.5 bit64_4.0.5 glue_1.6.2 loo_2.5.1 sctransform_0.3.5 rstan_2.21.8 parallel_4.2.2 processx_3.8.0 spatstat.sparse_3.0-0 AnnotationDbi_1.60.0 spatstat.geom_3.0-6 haven_2.5.1 tidyselect_1.2.0 fitdistrplus_1.1-8 XML_3.99-0.13 zoo_1.8-11 packrat_0.9.0 distributional_0.3.1 chron_2.3-59 xtable_1.8-4 magrittr_2.0.3 evaluate_0.20 gdtools_0.3.0 cli_3.6.0 zlibbioc_1.44.0 rstudioapi_0.14 miniUI_0.1.1.1 sp_1.6-0 whisker_0.4.1 bslib_0.4.2 shinystan_2.6.0 shiny_1.7.4 BiocSingular_1.14.0 xfun_0.37 askpass_1.1 inline_0.3.19 pkgbuild_1.4.0 cluster_2.1.4 bridgesampling_1.1-2 gfonts_0.2.0 KEGGREST_1.38.0 Brobdingnag_1.2-9 ggrepel_0.9.3 threejs_0.3.3 listenv_0.9.0 xlsxjars_0.6.1 Biostrings_2.66.0 png_0.1-8 future_1.31.0 withr_2.5.0 bitops_1.0-7 plyr_1.8.8 cellranger_1.1.0 coda_0.19-4 pillar_1.8.1 RcppParallel_5.1.6 cachem_1.0.6 fs_1.6.1 DelayedMatrixStats_1.20.0 xts_0.12.2 vctrs_0.5.2 ellipsis_0.3.2 generics_0.1.3 dygraphs_1.1.1.6 tools_4.2.2 munsell_0.5.0 DelayedArray_0.24.0 fastmap_1.1.0 compiler_4.2.2 abind_1.4-5 httpuv_1.6.9 plotly_4.10.1 rJava_1.0-6 GenomeInfoDbData_1.2.9 gridExtra_2.3 lattice_0.20-45 deldir_1.0-6 utf8_1.2.3 later_1.3.0 jsonlite_1.8.4 scales_1.2.1 ScaledMatrix_1.6.0 carData_3.0-5 pbapply_1.7-0 sparseMatrixStats_1.10.0 lazyeval_0.2.2 promises_1.2.0.1 car_3.1-1 goftest_1.2-3 spatstat.utils_3.0-1 reticulate_1.28 checkmate_2.1.0 rmarkdown_2.20 cowplot_1.1.1 textshaping_0.3.6 webshot_0.5.4 Rtsne_0.16 uwot_0.1.14 igraph_1.4.0 rsconnect_0.8.29 survival_3.5-3 yaml_2.3.7 systemfonts_1.0.4 bayesplot_1.10.0 htmltools_0.5.4 rstantools_2.2.0 memoise_2.0.1 locfit_1.5-9.7 digest_0.6.31 assertthat_0.2.1 mime_0.12 RSQLite_2.2.20 future.apply_1.10.0 blob_1.2.3 ragg_1.2.5 labeling_0.4.2 shinythemes_1.2.0 splines_4.2.2 googledrive_2.0.0 RCurl_1.98-1.10 broom_1.0.3 hms_1.1.2 modelr_0.1.10 colorspace_2.1-0 base64enc_0.1-3 BiocManager_1.30.19 nnet_7.3-18 sass_0.4.5 RANN_2.6.1 mvtnorm_1.1-3 fansi_1.0.4 tzdb_0.3.0 truncnorm_1.0-8 parallelly_1.34.0 R6_2.5.1 grid_4.2.2 crul_1.3 ggridges_0.5.4 lifecycle_1.0.3 StanHeaders_2.21.0-7 zip_2.2.2 writexl_1.4.2 curl_5.0.0 ggsignif_0.6.4 googlesheets4_1.0.1 leiden_0.4.3 jquerylib_0.1.4 Matrix_1.5-3 RcppAnnoy_0.0.20 spatstat.explore_3.0-6 htmlwidgets_1.6.1 officer_0.5.2 beachmat_2.14.0 polyclip_1.10-4 markdown_1.5 crosstalk_1.2.0 timechange_0.2.0 rvest_1.0.3 globals_0.16.2 openssl_2.0.5 patchwork_1.1.2 spatstat.random_3.1-3 tensorA_0.36.2 progressr_0.13.0 codetools_0.2-19 lubridate_1.9.2 gtools_3.9.4 getPass_0.2-2 prettyunits_1.1.1 dbplyr_2.3.0 gtable_0.3.1 DBI_1.1.3 git2r_0.31.0 highr_0.10 tensor_1.5 httr_1.4.4 KernSmooth_2.23-20 stringi_1.7.12 reshape2_1.4.4 farver_2.1.1 uuid_1.1-0 annotate_1.76.0 mice_3.15.0 xml2_1.3.3 shinyjs_2.1.0 geneplotter_1.76.0 scattermore_0.8 bit_4.0.5 spatstat.data_3.0-0 pkgconfig_2.0.3 gargle_1.3.0 rstatix_0.7.2 HardyWeinberg_1.7.5 Rsolnp_1.16 knitr_1.42 httpcode_0.3.0